Financial institutions are subject to many sources of risk, where risk often represents the degree of uncertainty about future net returns. Credit Risk can result in potential loss due to the inability of a counterparty to meet its obligations and is a function of the credit exposure, the...

The numerical calculations of marginal risk contributions associated with the two risk measures, Value-at-Risk and Expected Shortfall, pose challenges due to the rare event character of multiple defaults among obligors in credit portfolios at a high confidence level. We explore various...

When datasets present long conditional tails on their response variables, algorithms based on Quantile Regression have been widely used to assess extreme quantile behaviors. Value at Risk (VaR) and Conditional Tail Expectation (CTE) allow the evaluation of extreme events to be easily...

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the Bayesian methodology has significant advantages. Just...

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated...

In this paper, we propose a new method for estimating and forecasting asymmetric stochastic volatility models. The proposal is based on dynamic linear models with Markov switching written as state space models. Then, the likelihood is calculated through Kalman filter outputs and the estimates...